Publication: Testing feature selection in traffic signs
Loading...
Identifiers
Publication date
2007
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de Las Palmas de Gran Canaria
Impact
Export
Abstract
Road signs carry essential information for successful driving. Therefore, if we are interested in developing a Driver Support Systems, both, detection and classification of road signs are essential tasks for an autonomous system. However, both tasks are some of the less studied subjects in the field of Intelligent Transport systems. In this research we lay the foundations of a software implementation for a classifier system that will be implemented in hardware and will be able to be used for real-time traffic sign categorization. The selected classification method is a Multilayer Perceptron trained with Back-Propagation algorithm. The reason of this selection is, on one hand, that for certain types of problems, such as object recognition in natural environments, neural network learning methods provide a robust approach. On the other hand, and under certain, limitations related mainly to the number of units, a hardware implementation on FPGA of ANN is possible. Therefore, ANNs are a good method for real-time processing in real-word problems
Description
Keywords
Driver support systems, Intelligent Transportation Systems, Multilayer perceptron, Backpropagation algorithm, FPGA (Field Programmable Gate Array), ANN
Bibliographic citation
Computer aided systems theory (EUROCAST 2007): 11th International Conference on Computer Aided Systems Theory: Las Palmas de Gran Canaria, Spain, February 2007: extended abstracts. Las Palmas de Gran Canaria: Universidad, 2007, p. 396-398. ISBN 978-84-690-3603-7